CONSTRUCT VALIDITY AND RELIABILITY
4.2 Construct Assessment
Construct assessment was conducted and items for each construct were analysed individually using exploratory factor analysis (principal component analysis) and Cronbach’s alpha for internal consistency reliability. A summary of the construct assessment is provided below (a more detailed discussion was provided in Chapter 3):
In the exploratory factor analysis:
- Statistical Package for Social Sciences (SPSS) version 15 was utilised.
- All constructs were factor analysed with principal component analysis (varimax rotation) (Hair et al. 1998).
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- Only factors having eigenvalue above 1 were considered significant and retained (Hair et al. 1998).
- The cut-off point for item loading was 0.35 and any items below the desired cut- off were not displayed in the results (Hair et al. 1998).
- The consistency among the items was checked using Cronbach’s alpha (desired cut-off was 0.70) (Carmines & Zeller 1979; De Vellis 2003). In addition, internal consistency among the items was also checked by looking at the item-to-total correlation (desired cut-off 0.50) and inter-item correlation (desired cut-off 0.30) (Hair et al. 1998). Any items below the desired cut-off for Cronbach’s alpha, item-to-total correlation and inter-item correlation respectively were dropped.
In the confirmatory factor analysis:
- The Analysis of Moment Structure (AMOS) version 7 was utilised. - The measurement models for each construct were constructed.
- The measurement models were evaluated by examining the factor loading for each item. The desired cut-off for factor loading was taken to be at least 0.50 for adequate individual item reliability (Bagozzi & Yi 1988).
- The χ2statistic was used to measure the overall fit of the measurement models. In
this thesis, the researcher looked for non-significant differences (p>0.05 or p>0.01) because the test was between the actual and predicted matrices (Hair et al. 1998).
- Other fit indices utilised were Goodness of Fit Index (GFI), the Adjusted Goodness of Fit Index (AGFI), the Standardised Root Mean Square Residual (RMSR), the Tucker Lewis Index (TLI), the Comparative Fit Index (CFI), the Normed Fit Index (NFI) and the Root Mean Square Error of Approximation (RMSEA) (Hair et al. 1998). The recommended value for GFI, AGFI, TLI, CFI and NFI is 0.90 or greater where value less than 0.90 considered as poor fit (Hair et al. 1998). The RMSR should have value less than 0.10 where value equal to or greater than 0.10 would indicate poor fit (Kline 2005). The recommended value for RMSEA should be no more than 0.08 for reasonable error of approximation (Kline 2005; Hair et al. 1998).
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- When the measurement model did not show a good fit, the modification indices provided by AMOS version 7 were examined. Items were allowed to correlate when inspection found that they were redundant due to poor wording (Arbuckle & Wothe 1999; Joreskog & Sorbom 1993).
- Construct reliability and variance extracted were calculated using the formula provided by Hair et al. (1998) (see Chapter 3). The desired cut-off was 0.70 and 0.50 for construct reliability and variance extracted respectively (Fornell & Larcker 1981).
The next section details the outcomes of the tests conducted on each construct. In the first stage, principle component analysis was conducted followed by a reliability analysis and finally the measurement model for each construct was developed. In total there were five learner readiness items; four performance-self efficacy items; four motivation to transfer items; five transfer effort-performance expectations items; four performance-outcome expectations items; four feedback items; four peer support
items; four supervisor support items; four openness to change items; four personal outcomes-positive items; four personal outcomes-negative items; four supervisor sanctions items; three personal capacity for transfer items; four opportunity to use
items; five content validity items; four transfer design items; five sharing behaviour
items; four intention to share items; five attitude toward knowledge sharing items; four subjective norm toward knowledge sharing items; and three perceived behavioural control toward knowledge sharing items. A detailed description of the definition of each construct was presented in Table 1.1 in Chapter 1.
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4.2.1 The Learner Readiness Construct
Principal component analysis with varimax rotation was conducted on the five
learner readiness items. As expected, only one factor was extracted (eigenvalue above 1) which accounted for approximately 55.36 percent of the total variance, confirming the unidimensionality of this construct (De Vellis 1991; Hair et al. 1998). All items had factor loading above the recommended cut-off 0.35 (Hair et al. 1998) (see Table 4.1). Therefore, no items were dropped following this stage of item testing.
Table 4.1 Learner Readiness Principal Component Analysis
Items Factor : Learner Readiness
Q1. I know that this training is good for me. 0.66 Q2. I applied for this training on my own. 0.58 Q5. I am definitely interested to join this training. 0.83 Q8. I am definitely ready to join this training. 0.80 Q9. I knew that I would obtain something beneficial from this
training. 0.81 Number of cases 291 Eigenvalue 2.77 Percentage of Variance 55.36 Cronbach’s alpha 0.73
Reliability analysis was also conducted with these items. The results indicated that all items had item-total correlation above 0.50 except two items (Q1=0.47 and Q2=0.41) (see Appendix J) which were slightly below the recommended level of 0.50. Further inspection on the inter-item correlation matrix revealed that all items were above the recommended cut-off, 0.30 (Hair et al. 1998) (see Appendix J). The Cronbach’s alpha was calculated at 0.73, also above the recommended cut-off, 0.70 (Carmines & Zeller 1979; De Vellis 2003) (see Appendix J). Therefore, no items were dropped for this scale.
Next, the measurement model for the learner readiness construct was constructed with the five items as depicted in Figure 4.1.
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Learner Readiness Construct
Table 4.2 Fit Indices for Learner Readiness Construct
χ2 value 7.519 RMSR 0.011
Degrees of freedom 5 TLI 0.988
p value 0.185 CFI 0.994
GFI 0.989 NFI 0.983
AGFI 0.967 RMSEA 0.042
The measurement model for the learner readiness construct produced a good model fit to the data with a non-significant χ2 statistic (p>0.05), indicating that the actual and predicted input matrices were not statistically different (Hair et al. 1998). The factor loading for all items were also above the recommended cut-off, 0.50 indicating adequate individual item reliability (Bagozzi & Yi 1988). There was one exception: q2=0.45. However, despite the exception, this item was retained because the fit indices supported a good fit with GFI, AGFI, NFI, TLI and CFI above the desired cut-off, 0.90 (Hair et al. 1998). The RMSR and RMSEA were below the recommended levels of 0.10 and 0.08 respectively (Kline 2005) (see Table 7.23). The construct reliability was above the recommended level 0.70 (Hair et al. 1998) and the variance extracted by the construct was slightly below the recommended 0.50 cut-off due to measurement error (Fornell & Larcker 1981) (see Table 4.3). Despite the
Learner readiness .55 q9 e5 .74 .58 q8 e4 .76 .66 q5 e3 .81 .20 q2 e2 .45 .29 q1 e1 .54
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slightly lower variance extraction, principal component analysis with varimax rotation revealed that the variance extracted was 55.36 percent, clearly above the 50 percent, indicating that this construct possessed adequate reliability and validity.
Table 4.3 Descriptive Statistics, Cronbach’s Alpha, Construct Reliability and Variance Extracted for Learner Readiness Construct
Construct Mean Standard Deviation (δδδδ) Cronbach’s Alpha (αααα) Construct
Reliability Extracted Variance
Motivation
to transfer 4.422 0.528 0.731 0.80 0.455 Note: see Appendix K for construct reliability and variance extracted workings.
4.2.2 The Performance-Self Efficacy Construct
Principal component analysis with varimax rotation was conducted on the four
performance-self efficacy items. Results indicated that only one factor was extracted (eigenvalue above 1), confirming the unidimensionality of this construct (De Vellis 1991; Hair et al. 1998). The percentage of variance extracted was approximately 67.16 percent. All items had factor loadings above the recommended cut-off, 0.35 (Hair et al. 1998) (see table 4.4). Therefore, no items were dropped following this stage of item testing..
Table 4.4 Performance-Self Efficacy Principal Component Analysis
Items Factor: Performance-Self Efficacy
Q59. I am confidence to increase my job performance. 0.74 Q60. I have the capabilities to increase my job performance. 0.82 Q63. I am confident that I can improve my job performance
because I am a discipline person. 0.85 Q64. I am confident that I can improve my job performance
because I am a hardworking person. 0.85
Number of cases 291
Eigenvalue 2.69
Percentage of Variance 67.16
Cronbach’s alpha 0.83
Following this, reliability analysis was conducted with these items. The results indicated that all items displayed item-total correlation and inter-item correlation above the desired cut-offs, 0.50 and 0.30 respectively (Hair et al. 1998) (see
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Appendix J). Cronbach’s alpha was 0.83, also above the recommended cut-off, 0.70 (Carmines & Zeller 1979; De Vellis 2003) (see Appendix J). Therefore, no items were dropped from this scale.
Next, the measurement model for performance-self efficacy construct was dimensioned with the four items as depicted in Figure 4.2 below.
Figure 4.2 Measurement Model: Performance-Self Efficacy Construct